arXiv (All)
open access
Alpha: Systematic Fault Injection and Robustness Evaluation for LLM-Driven Embodied Manipulation
Large language models (LLMs) are increasingly integrated into embodied robotic planning, yet standard benchmarks primarily evaluate end-to-end task completion in nominal environments, obscuring failure origins across the execution stack. We present Alpha, a systematic fault-injection and robustness-
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ROOT: Discovering Rewards for User-Specified Embodied Behaviors#1199524
- TESS: LLM-guided Translation, Exploration, Simulation, and Search for Embodied Task Planning#1401119
- EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks#1064780
- RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents#204722
- "Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation#128408
Record · ID 1422989
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive